Szczegóły publikacji
Opis bibliograficzny
Diagnosing faults in electric motors using B-splines and Bayesian Mixture Model / Kacper Jarzyna, Bartłomiej GAWĘDA, Jerzy BARANOWSKI // W: MMAR 2024 [Dokument elektroniczny] : 2024 28th international conference on Methods and Models in Automation and Robotics : 27–30 August 2024, Międzyzdroje, Poland : technical papers : on line proceedings. — Wersja do Windows. — Dane tekstowe. — Danvers : IEEE, cop. 2024. — (International Conference on Methods and Models in Automation and Robotics ; ISSN 2835-2815). — e-ISBN: 979-8-3503-6233-6. — S. 288–292. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 292, Abstr. — Publikacja dostępna online od: 2024-09-19. --- Abstrakt w: MMAR 2024 : 28th international conference on Methods and Models in Automation and Robotics : 27--30 August 2024, Międzyzdroje, Poland : program - abstracts. --- S. 49--50
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 155085 |
|---|---|
| Data dodania do BaDAP | 2024-09-25 |
| Tekst źródłowy | URL |
| DOI | 10.1109/MMAR62187.2024.10680782 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | International Conference on Methods and Models in Automation and Robotics 2024 |
| Czasopismo/seria | International Conference on Methods and Models in Automation and Robotics |
Abstract
Diagnosing faults in electric motors is crucial for various applications, from everyday devices to industrial machinery. Authors propose a method for identifying motor faults using acoustic signals, which are easy to capture with microphones. Proposed approach involves analyzing these signals using Functional Data Analysis (FDA), representing frequency patterns with B-splines and Bayesian Mixture Model as classifier. In this paper, there was developed a classifier to categorize five motor fault types based on these transformed signals. By focusing on frequencies up to 2500 Hz relevant to motor issues, authors aim to detect faults without needing complex equipment and greatly shorten computation time. This approach yields promising results.
